A template-matching pandemonium recognizes unconstrained handwritten characters with high accuracy

Abstract

Psychological data suggest that internal representations such as mental images can be used as templates in visual pattern recognition. But computational studies suggest that traditional template matching is insufficient for high-accuracy recognition of real-life patterns such as handwritten characters. Here we explore a model for visual pattern recognition that combines a template-matching and a feature-analysis approach: Character classification is based on weighted evidence from a number of analyzers (demons), each of which computes the degree of match between the input character and a stored template (a copy of a previously presented character). The template-matching pandemonium was trained to recognize totally unconstrained handwritten digits. With a mean of 37 templates per type of digit, the system has attained a recognition rate of 95.3%, which falls short of human performance by only 2%-3%.